1. Field of the Invention
The present invention is directed to a method as well as to an apparatus for filtering a digital image acquired with a medical device by means of a spatial-frequency operator.
2. Description of the Prior Art
With the increasing spread of digital X-ray apparatuses and computed tomography systems, images of an examination subject (for example, a patient) acquired with a medical device are increasingly in digital form and are thus accessible to digital image processing.
The medical devices usually provide the digital images in the form of an image matrix. A digital image is thus composed of a raster, with the number of rows and columns of the raster corresponding to the number of rows and columns of the picture elements (pixels) of the digital image. In, for example, a grayscale image (such as, for example, a digital X-ray image), exactly one grayscale value is allocated to each raster value, and thus to each picture element. Modern medical devices typically can distinguish up to 4,096 grayscale values per picture element.
Color images are represented by a number of such image matrices wherein the respective distribution of the irradiation intensity of a primary color is stored. Fundamentally, thus, the same processing techniques of digital image processing that are applicable for grayscale images can be applied to color images.
Filters play a key part in digital image processing. A filter is generally understood as an operator that generates a filtered image when it is applied to a source image.
Filters particularly serve for extracting desired information from an image. Since certain properties of an image thus are emphasized by means of filtering, but filtering is always accompanied by an information loss since other properties of the image are placed in the background.
Simple operators that, for example, implement the calculation of in image negative of the source image successively consider only the values of the individual picture elements of the source image and calculate the corresponding values of the individual picture elements of the filtered image therefrom.
More complex filters (for example filters that sharpen or soften (smooth, blur), in contrast, place the values of the individual picture elements of the source image into relationship with the values of the picture elements surrounding them and calculate respective picture elements of the filtered image therefrom. Such more complex filters are generally represented in the form of a filter matrix. The central component of the filter matrix is the function that is applied to the particular picture element of the source image that corresponds to the picture element of the filtered image to be calculated. The functions surrounding the central component of the matrix are applied to the picture elements that surround the respective picture element of the source image. The picture element to be calculated for the filtered image is then derived from the sum of the functions of the filter matrix applied to the respective picture elements.
FIG. 3 schematically shows the application of such a filter matrix 31 to a digital source image 32.
As can be seen from FIG. 3, the value of the sought picture element (indicated by double hatching) of the filtered image 33 is derived by operating on the corresponding picture element in the source image 32 with a filter matrix 31. The filter matrix 31 thus also determines which surrounding picture elements are also included in the operation. In the example of FIG. 3, this results in a value 1/32[5(−2)+7(−2)+4(−2)+3(−2)+3·48+8(−2)+3(−2)+6(2)]=2
As indicated, this calculation is implemented for each picture element of the source image 32 in order to obtain all picture elements of the filtered image 33. For simplicity, the values of the other picture elements of the filtered image 33 that have already been calculated are not explicitly calculated in FIG. 3 but are referenced “X”.
As also can be seen from this example, edge regions of the source image 32 are problematical in the filtering since the filter matrix 31 extends beyond the edge region of the source image when applied to respective picture elements in the edge region. There are various possibilities for solving this problem such as attaching picture elements from the opposite side of the image or an extrapolation beyond the edge of the image. Naturally, however, these methods are also affected by errors.
The filters that are most frequently employed for filtering a digital image acquired with a medical device can be divided into two categories:
Those referred to as “sharpening” filters are the most widespread in the medical field and produce an edge boosting in the filtered image.
Those referred to as “softening” filters produce a noise suppression in the filtered image.
The filter effects of the sharpening and of the softening filters are opposite, so that the application of a sharpening filter also unavoidably results in the quantum noise, that is always present in the source image being likewise intensified. Correspondingly, the edge sharpness of the source image decreases given the application of a softening filter.
It is clear from the above that no filter can be described that is optimum for all applications since different filter properties (for example, edge enhancement or noise suppression) are always of primary interest dependent on the quality and type of the digital (source) image and the applied purpose.
For solving this problem, it is known to make a group of suitable filters available with respectively different, subjective softening or sharpening effects. A certain pre-selection of employable filters from this group is made through the operating mode of the medical device and an evaluating physician can select therefrom.
A disadvantage of this approach is that the physician must often evaluate a number of unsuitable images until an optimum filter can be found. It is also disadvantageous that this procedure consumes a great deal of time, since the repeated filtering of the source image with various filters takes a certain time due to the calculating outlay connected therewith.
It is also known to prescribe a standard filter function with which all source images are filtered and to vary the filter effect by adjustment of the degree of the source image that participates in the filtering. The adjustment can ensue, for example, by selectively weighting the source image to be operated on.
A disadvantage of this approach, however, is that the standard filter function can be defined only inadequately defined since it is not equally well-suited for all operating modes of the medical device and for all test subjects that come into consideration. Another disadvantage is that a certain minimum number of unsuitable images again must be evaluated until an optimum filter effect can be found.